249
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

&
Pages 629-651 | Received 05 Jan 2023, Accepted 17 Mar 2023, Published online: 10 Apr 2023

References

  • Alves, C., & Diederich, M. (2021). Marine natural products as anticancer agents. Marine Drugs, 19(8), 447. https://doi.org/10.3390/md19080447
  • Archontis, G., & Hayes, J. M. (2012). MM-GB(PB)SA calculations of protein-ligand binding free energies. In Molecular Dynamics - Studies of Synthetic and Biological Macromolecules. InTech.
  • Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
  • Budak, C., Mençik, V., & Gider, V. (2023). Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method. Journal of Biomolecular Structure & Dynamics, 41(2), 659–671. https://doi.org/10.1080/07391102.2021.2010601
  • Chen, D., Oezguen, N., Urvil, P., Ferguson, C., Dann, S. M., & Savidge, T. C. (2016). Regulation of protein-ligand binding affinity by hydrogen bond pairing. Science Advances, 2(3), e1501240. https://doi.org/10.1126/sciadv.1501240
  • Davis, C. K., Nampoothiri, S. S., & Rajanikant, G. K. (2018). Folic acid exerts post-ischemic neuroprotection in vitro through HIF-1α stabilization. Molecular Neurobiology, 55(11), 8328–8345. https://doi.org/10.1007/s12035-018-0982-3
  • Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. In arXiv [cs.LG]. http://arxiv.org/abs/1509.09292
  • Eslavath, R. K., Sharma, D., Omar, N. A. M. B., Chikati, R., Teli, M. K., Rajanikant, G. K., & Singh, S. S. (2016). β-N-oxalyl-L-α, β-diaminopropionic acid induces HRE expression by inhibiting HIF-prolyl hydroxylase-2 in normoxic conditions. European Journal of Pharmacology, 791, 405–411. https://www.sciencedirect.com/science/article/pii/S0014299916304381 https://doi.org/10.1016/j.ejphar.2016.07.007
  • Fernandez, M., Ban, F., Woo, G., Hsing, M., Yamazaki, T., LeBlanc, E., Rennie, P. S., Welch, W. J., & Cherkasov, A. (2018). Toxic colors: The use of deep learning for predicting toxicity of compounds merely from their graphic images. Journal of Chemical Information and Modeling, 58(8), 1533–1543. https://doi.org/10.1021/acs.jcim.8b00338
  • Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. Journal of Medicinal Chemistry, 49(21), 6177–6196. https://doi.org/10.1021/jm051256o
  • Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., Davies, M., Dedman, N., Karlsson, A., Magariños, M. P., Overington, J. P., Papadatos, G., Smit, I., & Leach, A. R. (2017). The ChEMBL database in 2017. Nucleic Acids Research, 45(D1), D945–D954. https://doi.org/10.1093/nar/gkw1074
  • Gawehn, E., Hiss, J. A., Brown, J. B., & Schneider, G. (2018). Advancing drug discovery via GPU-based deep learning. Expert Opinion on Drug Discovery, 13(7), 579–582. https://doi.org/10.1080/17460441.2018.1465407
  • Gawehn, E., Hiss, J. A., & Schneider, G. (2016). Deep learning in drug discovery. Molecular Informatics, 35(1), 3–14. https://doi.org/10.1002/minf.201501008
  • Gider, V., & Budak, C. (2022). Instruction of molecular structure similarity and scaffolds of drugs under investigation in ebola virus treatment by atom-pair and graph network: A combination of favipiravir and molnupiravir. Computational Biology and Chemistry, 101, 107778. https://doi.org/10.1016/j.compbiolchem.2022.107778
  • Goh, G. B., Siegel, C., Vishnu, A., & Hodas, N. O. (2017). a). Using rule-based labels for weak supervised learning: A ChemNet for transferable chemical property prediction. In arXiv [stat.ML]. http://arxiv.org/abs/1712.02734
  • Goh, G. B., Siegel, C., Vishnu, A., Hodas, N. O., & Baker, N. (2017). Chemception: A deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. In arXiv [stat.ML]. http://arxiv.org/abs/1706.06689
  • González-Almela, E., Sanz, M. A., García-Moreno, M., Northcote, P., Pelletier, J., & Carrasco, L. (2015). Differential action of pateamine A on translation of genomic and subgenomic mRNAs from Sindbis virus. Virology, 484, 41–50. https://doi.org/10.1016/j.virol.2015.05.002
  • Gupta, M. K., Vemula, S., Donde, R., Gouda, G., Behera, L., & Vadde, R. (2021). In-silico approaches to detect inhibitors of the human severe acute respiratory syndrome coronavirus envelope protein ion channel. Journal of Biomolecular Structure & Dynamics, 39(7), 2617–2627. https://doi.org/10.1080/07391102.2020.1751300
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. 27–28. https://doi.org/10.1016/0263-7855(96)00018-5
  • Jimenez-Carretero, D., Abrishami, V., Fernández-de-Manuel, L., Palacios, I., Quílez-Álvarez, A., Díez-Sánchez, A., Del Pozo, M. A., & Montoya, M. C. (2018). Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening. PLoS Computational Biology, 14(11), e1006238. https://doi.org/10.1371/journal.pcbi.1006238
  • Krishnan, R., Rajpurkar, P., & Topol, E. J. (2022). Self-supervised learning in medicine and healthcare. Nature Biomedical Engineering, 6(12), 1346–1352. https://doi.org/10.1038/s41551-022-00914-1
  • Ladbury, J. E. (1996). Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chemistry & Biology, 3(12), 973–980. https://doi.org/10.1016/s1074-5521(96)90164-7
  • Lee, J. W., Ko, J., Ju, C., & Eltzschig, H. K. (2019). Hypoxia signaling in human diseases and therapeutic targets. Experimental & Molecular Medicine, 51(6), 1–13. https://doi.org/10.1038/s12276-019-0235-1
  • Li, X.-B., Wang, S.-Q., Xu, W.-R., Wang, R.-L., & Chou, K.-C. (2011). Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method. PloS One, 6(11), e28111. https://doi.org/10.1371/journal.pone.0028111
  • Li, Z., Jiang, M., Wang, S., & Zhang, S. (2022). Deep learning methods for molecular representation and property prediction. Drug Discovery Today, 27(12), 103373. https://doi.org/10.1016/j.drudis.2022.103373
  • Li, Z., Zhou, W., Zhang, Y., Sun, W., Yung, M. M. H., Sun, J., Li, J., Chen, C.-W., Li, Z., Meng, Y., Chai, J., Zhou, Y., Liu, S. S., Cheung, A. N. Y., Ngan, H. Y. S., Chan, D. W., Zheng, W., & Zhu, W. (2019). ERK regulates HIF1α-mediated platinum resistance by directly targeting PHD2 in ovarian cancer. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 25(19), 5947–5960. https://doi.org/10.1158/1078-0432.CCR-18-4145
  • Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today. Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
  • Lipinski, C. F., Maltarollo, V. G., Oliveira, P. R., da Silva, A. B. F., & Honorio, K. M. (2019). Advances and perspectives in applying deep learning for drug design and discovery. Frontiers in Robotics and AI, 6, 108. https://doi.org/10.3389/frobt.2019.00108
  • Lyu, C., Chen, T., Qiang, B., Liu, N., Wang, H., Zhang, L., & Liu, Z. (2021). CMNPD: A comprehensive marine natural products database towards facilitating drug discovery from the ocean. Nucleic Acids Research, 49(D1), D509–D515. https://doi.org/10.1093/nar/gkaa763
  • Mullard, A. (2023). FDA approves first hypoxia-inducible factor prolyl hydroxylase inhibitor. Nature Reviews Drug Discovery, 22(3), 173–173. https://doi.org/10.1038/d41573-023-00028-6
  • Olenchock, B. A., Moslehi, J., Baik, A. H., Davidson, S. M., Williams, J., Gibson, W. J., Chakraborty, A. A., Pierce, K. A., Miller, C. M., Hanse, E. A., Kelekar, A., Sullivan, L. B., Wagers, A. J., Clish, C. B., Vander Heiden, M. G., & Kaelin, W. G. Jr (2016). EGLN1 inhibition and rerouting of α-ketoglutarate suffice for remote ischemic protection. Cell, 164(5), 884–895. https://doi.org/10.1016/j.cell.2016.02.006
  • Papon, N., Copp, B. R., & Courdavault, V. (2022). Marine drugs: Biology, pipelines, current and future prospects for production. Biotechnology Advances, 54(, 107871. https://doi.org/10.1016/j.biotechadv.2021.107871
  • Peters, T. L., Tillotson, J., Yeomans, A. M., Wilmore, S., Lemm, E., Jiménez-Romero, C., Amador, L. A., Li, L., Amin, A. D., Pongtornpipat, P., Zerio, C. J., Ambrose, A. J., Paine-Murrieta, G., Greninger, P., Vega, F., Benes, C. H., Packham, G., Rodríguez, A. D., Chapman, E., & Schatz, J. H. (2018). Target-based screening against eIF4A1 reveals the marine natural product elatol as a novel inhibitor of translation initiation with in vivo antitumor activity. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 24(17), 4256–4270. https://doi.org/10.1158/1078-0432.CCR-17-3645
  • Piroozmand, F., Mohammadipanah, F., & Sajedi, H. (2020). Spectrum of deep learning algorithms in drug discovery. Chemical Biology & Drug Design, 96(3), 886–901. https://doi.org/10.1111/cbdd.13674
  • Ramachandran, B., Kesavan, S., & Rajkumar, T. (2016). Molecular modeling and docking of small molecule inhibitors against NEK2. Bioinformation, 12(2), 62–68. https://doi.org/10.6026/97320630012062
  • Reddy, K. K., & Singh, S. K. (2015). Insight into the binding mode between N-methyl pyrimidones and prototype foamy virus integrase-DNA complex by QM-polarized ligand docking and molecular dynamics simulations. Current Topics in Medicinal Chemistry, 15(1), 43–49. https://doi.org/10.2174/1568026615666150112110948
  • Reddy, K. K., Singh, P., & Singh, S. K. (2014). Blocking the interaction between HIV-1 integrase and human LEDGF/p75: Mutational studies, virtual screening and molecular dynamics simulations. Molecular bioSystems, 10(3), 526–536. https://doi.org/10.1039/c3mb70418a
  • Reggiani, F., Sauta, E., Torricelli, F., Zanetti, E., Tagliavini, E., Santandrea, G., Gobbi, G., Damia, G., Bellazzi, R., Ambrosetti, D., Ciarrocchi, A., & Sancisi, V. (2021). An integrative functional genomics approach reveals EGLN1 as a novel therapeutic target in KRAS mutated lung adenocarcinoma. Molecular Cancer, 20(1), 63. https://doi.org/10.1186/s12943-021-01357-z
  • Rifaioglu, A. S., Nalbat, E., Atalay, V., Martin, M. J., Cetin-Atalay, R., & Doğan, T. (2020). DEEPScreen: High performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chemical Science, 11(9), 2531–2557. https://doi.org/10.1039/c9sc03414e
  • Selvaraj, C., Omer, A., Singh, P., & Singh, S. K. (2015). Molecular insights of protein contour recognition with ligand pharmacophoric sites through combinatorial library design and MD simulation in validating HTLV-1 PR inhibitors. Molecular bioSystems, 11(1), 178–189. https://doi.org/10.1039/c4mb00486h
  • Semenza, G. L. (2003a). Angiogenesis in ischemic and neoplastic disorders. Annual Review of Medicine, 54(1), 17–28. https://doi.org/10.1146/annurev.med.54.101601.152418
  • Semenza, G. L. (2003b). Targeting HIF-1 for cancer therapy. Nature Reviews. Cancer, 3(10), 721–732. https://doi.org/10.1038/nrc1187
  • Semenza, G. L. (2016). Targeting hypoxia-inducible factor 1 to stimulate tissue vascularization. Journal of Investigative Medicine: The Official Publication of the American Federation for Clinical Research, 64(2), 361–363. https://doi.org/10.1097/JIM.0000000000000206
  • Semenza, G. L. (2023). Regulation of erythropoiesis by the hypoxia-inducible factor pathway: Effects of genetic and pharmacological perturbations. Annual Review of Medicine, 74(1), 307–319. https://doi.org/10.1146/annurev-med-042921-102602
  • Sharma, V., & Wakode, S. (2017). Structural insight into selective phosphodiesterase 4B inhibitors: Pharmacophore-based virtual screening, docking, and molecular dynamics simulations. Journal of Biomolecular Structure & Dynamics, 35(6), 1339–1349. https://doi.org/10.1080/07391102.2016.1183520
  • Sharp, F. R., & Bernaudin, M. (2004). HIF1 and oxygen sensing in the brain. Nature Reviews. Neuroscience, 5(6), 437–448. https://doi.org/10.1038/nrn1408
  • Singh, S., Vijaya Prabhu, S., Suryanarayanan, V., Bhardwaj, R., Singh, S. K., & Dubey, V. K. (2016). Molecular docking and structure-based virtual screening studies of potential drug target, CAAX prenyl proteases, of Leishmania donovani. Journal of Biomolecular Structure & Dynamics, 34(11), 2367–2386. https://doi.org/10.1080/07391102.2015.1116411
  • Song, T., Zhong, Y., Ding, M., Zhao, R., Tian, Q., Du, Z., Liu, D., Liu, J., & Deng, Y. (2020). Repositioning molecules of Chinese medicine to targets of SARS-cov-2 by deep learning method [Paper presentation].2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)., https://doi.org/10.1109/BIBM49941.2020.9313151
  • Stephenson, N., Shane, E., Chase, J., Rowland, J., Ries, D., Justice, N., Zhang, J., Chan, L., & Cao, R. (2019). Survey of machine learning techniques in drug discovery. Current Drug Metabolism, 20(3), 185–193. https://doi.org/10.2174/1389200219666180820112457
  • Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., Tran, V. M., Chiappino-Pepe, A., Badran, A. H., Andrews, I. W., Chory, E. J., Church, G. M., Brown, E. D., Jaakkola, T. S., Barzilay, R., & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.e13. https://doi.org/10.1016/j.cell.2020.01.021
  • Teli, M. K., & Krishnamurthy, R. G. (2013a). Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. Journal of Chemical Information and Modeling, 53(7), 1818–1824. https://doi.org/10.1021/ci400254a
  • Teli, M. K., & Krishnamurthy, R. G. (2013b). ). A combination of 3D-QSAR modeling and molecular docking approach for the discovery of potential HIF prolyl hydroxylase inhibitors. Medicinal Chemistry (Shariqah (United Arab Emirates)), 9(3), 360–370. https://doi.org/10.2174/1573406411309030006
  • Tripathi, S. K., & Singh, S. K. (2014). Insights into the structural basis of 3,5-diaminoindazoles as CDK2 inhibitors: Prediction of binding modes and potency by QM-MM interaction, MESP and MD simulation. Molecular bioSystems, 10(8), 2189–2201. https://doi.org/10.1039/c4mb00077c
  • Wang, S., Du, Z., Ding, M., Zhao, R., Rodriguez-Paton, A., & Song, T. (2020). LDCNN-DTI: A novel light deep convolutional neural network for drug-target interaction predictions [Paper presentation].2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)., https://doi.org/10.1109/BIBM49941.2020.9313585
  • Watts, D., Bechmann, N., Meneses, A., Poutakidou, I. K., Kaden, D., Conrad, C., Krüger, A., Stein, J., El-Armouche, A., Chavakis, T., Eisenhofer, G., Peitzsch, M., & Wielockx, B. (2021). HIF2α regulates the synthesis and release of epinephrine in the adrenal medulla. Journal of Molecular Medicine (Berlin, Germany), 99(11), 1655–1666. https://doi.org/10.1007/s00109-021-02121-y
  • Wilkins, S. E., Abboud, M. I., Hancock, R. L., & Schofield, C. J. (2016). Targeting protein-protein interactions in the HIF system. ChemMedChem. 11(8), 773–786. https://doi.org/10.1002/cmdc.201600012
  • Zhang, Y., Ye, T., Xi, H., Juhas, M., & Li, J. (2021). Deep learning driven drug discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2. Frontiers in Microbiology, 12, 739684. https://doi.org/10.3389/fmicb.2021.739684
  • Zheng, Q., Yang, H., Sun, L., Wei, R., Fu, X., Wang, Y., Huang, Y., Liu, Y. N., & Liu, W. J. (2020). Efficacy and safety of HIF prolyl-hydroxylase inhibitor vs epoetin and darbepoetin for anemia in chronic kidney disease patients not undergoing dialysis: A network meta-analysis. Pharmacological Research, 159(, 105020. https://doi.org/10.1016/j.phrs.2020.105020

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.